The present invention relates generally to mobile advertising and more specifically to personalized behavioral targeting of mobile advertisements.
1. Technical Field
The present invention relates generally to mobile advertising and more specifically to personalized behavioral targeting of mobile advertisements.
2. Description of the Related Art
Mobile data usage is increasing, due to the availability of higher speed networks, more capable and advanced devices, and increasing trend towards operators offering flat-rate data plans. As a result, there is a growing momentum towards using rich media content on mobile devices. This opens up a huge possibility of advertising and personalization around this mobile content. What makes mobile advertising even more attractive than other digital advertising on the Internet is that phones are highly personal and mobile devices. In other words, a phone is associated with a specific individual (unlike a “family” PC) and travels with the user (unlike the home or work PC being used in specific environments). As a result, there is a huge potential to target advertisements to a specific user through personalization based on demographics, usage patterns, and location. As the user moves to different places, the advertisements can be tailored to the location to deliver ads that are very relevant to the location. In addition, the ads can also be targeted to match the user's behavior, based on history as well characteristics of the user's device and network. For instance, it is possible to serve ads based on past history of transactions or interests. Further, since a mobile user often uses several applications, ads can be targeted across applications such as SMS, Web browsing, Video, Voice, Search, etc. Also since the mobile is a ‘communication’ device, it is possible to infer communication networks around a user to insert appropriate ads across a community of users. Other dimensions include the user's tariff plan—for instance, post paid users with unlimited data plans are attractive for different types of advertisements than a prepaid user. This concept of personalization based on user-level mobile information is referred to as “Mobile Behavioral Targeting”.
The state of Mobile Advertising today does not allow such personalization since mobile information cannot be effectively leveraged. The data required for such personalization exists in silos in the mobile network. There is a lack of techniques to correlate the data across different dimensions. Further, there is also no known methodology for analyzing this information to determine the best parameters to be fed to get the best targeting and for inserting this information, in real-time, into existing applications. In addition, privacy issues need to be adhered to. The preferred embodiment of the invention describes a method to enable such mobile behavioral targeting, while maintaining privacy.
Prior art can be grouped into two categories: (a) internet-style behavioral targeting (b) mobile ad networks and campaign management platforms.
Behavioral targeting has been explored for on-line PC users, but those techniques don't apply in the context of mobile targeted ads because new techniques are required to capture the information required for behavioral targeting in the mobile world. The preferred embodiment of the invention describes methods to capture relevant mobile specific data and to correlate it to generate targeting data.
The mobile advertising platforms today are in their early stage where the focus is on creation of campaigns and not on targeting. As these evolve into targeting, they need new methods to insert ads into the mobile applications, Existing methods used on the internet don't apply. The preferred embodiment of the invention describes methods to insert this targeting information into different mobile applications for targeting.
These two aspects of the challenges in the prior art are described in detail next.
Challenges with extending internet-style targeting for mobile: Many of the standard PC approaches used on the Internet today (e.g. Tacoda, Revenue Science, AlmondNet) track user behavior on the network through cookies, action tags, and clickstream information for a set of participating publishers. They collect information obtained from cookies and action tags from participating publishers and perform click stream analysis to get information about users. These profiles are then used for targeting, typically by passing data through cookies. Such a cookie based tracking and targeting approach doesn't work on mobile networks. Cookies can't be generalized across all mobile devices. Many of the non-smartphone devices don't support cookies. Further, cookies on mobile devices are often deleted or stripped by gateways. Also, the information used by these internet targeting approaches is restricted to usage history for the set of sites they track. Not only are they limited because of the set of sites, but they also don't layer in other pieces of information such as location or demographics because this data is not usually accurately available for the Internet. Demographics information accuracy depends on their heuristics or subscriber disclosure—which may not be very accurate. Some point services (e.g. Quova Geopoint, Digital Envoy Geo-lntelligence, or Digital Island TraceWare) map IP addresses to location, but these techniques don't work on mobiles since often times mobile networks mask IP addresses and user locations keep changing, so static IP addresses are not relevant.
There are other recent players that are addressing behavioral targeting on the Internet through network based solutions—e.g. FrontPorch, NebuAd, Project Realto, etc. These approaches work with ISPs to monitor all user traffic and then correlate accesses to generate user behavioral profiles. While these approaches eliminate cookies, these approaches are restricted to the usage dimension and don't consider other parameters such as location. Also, these tend to be limited to Web applications, and don't extend well across mobile applications.
Thus, in general terrestrial internet approaches don't work on the mobile since techniques used in the PC world for getting further information through client side scripts and cookies etc. are not universal on mobile phones. Further, mobile approaches can take advantage of other information that is unique to mobiles, such as location and precise demographic data. Since this information is not available on the PC, the internet specific approaches don't use this data.
Challenges with existing mobile advertising platforms and why they don't do mobile behavioral targeting: Existing mobile ad platforms (e.g. ThirdScreen Media (AOL), AdMob, Rhythm New Media, Millennial Media, DoubleClick, Amobee, etc.) are focused on a methodology to create ad campaigns and deliver basic ads. These ads are usually either contextual or based on some basic data. For instance, a contextual ad relies on the context of the page that is being viewed—it is not targeted to the specific user. In some cases, a carrier may provide some basic data such as demographics to a subset of sites. In this case, the ad can be targeted based on this limited data. However, to achieve the full potential of targeting such a white-list approach is not sufficient. For one, only limited data is available this way. Second, this approach also suffers from privacy problems. In order to accomplish behavioral targeting, ad networks need rich multi-dimensional targeting info. This data is within the mobile network and requires technologies to collect, mine, correlate, and broker this data securely. The preferred embodiment of the invention describes a method to provide additional information (e.g. location, demographics, usage history, etc.) to the ad selection process so that ads can be better targeted to the user. The preferred embodiment of the invention leverages the data collection techniques described in U.S. application Ser. No. 12/324,671, U.S. application Ser. No. 12/324,672, and U.S. application Ser. No. 12/324,675.